Evaluation of interleukin‐1 and interleukin‐6 receptor antagonists in a murine model of acute lung injury
Bibliographic record
Abstract
Abstract The acute exudative phase of acute respiratory distress syndrome (ARDS), a severe form of respiratory failure, is characterized by alveolar damage, pulmonary oedema, and an exacerbated inflammatory response. There is no effective treatment for this condition, but based on the major contribution of inflammation, anti‐inflammatory strategies have been evaluated in animal models and clinical trials, with conflicting results. In COVID‐19 ARDS patients, interleukin (IL)‐1 and IL‐6 receptor antagonists (IL‐1Ra and IL‐6Ra, kineret and tocilizumab, respectively) have shown some efficacy. Moreover, we have previously developed novel peptides modulating IL‐1R and IL‐6R activity (rytvela and HSJ633, respectively) while preserving immune vigilance and cytoprotective pathways. We aimed to assess the efficacy of these novel IL‐1Ra and IL‐6Ra, compared to commercially available drugs (kineret, tocilizumab) during the exudative phase (day 7) of bleomycin‐induced acute lung injury (ALI) in mice. Our results first showed that none of the IL‐1Ra and IL‐6Ra compounds attenuated bleomycin‐induced weight loss and venous increase. Histological analyses and lung water content measurements also showed that these drugs did not improve lung injury scores or pulmonary oedema, after the bleomycin challenge. Finally, IL‐1Ra and IL‐6Ra failed to alleviate the inflammatory status of the mice, as indicated by cytokine levels and alveolar neutrophil infiltration. Altogether, these results indicate a lack of beneficial effects of IL‐1R and IL‐6R antagonists on key parameters of ALI in the bleomycin mouse model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".